diff --git a/train_model.py b/train_model.py
index f08b84e..9127ec8 100644
--- a/train_model.py
+++ b/train_model.py
@@ -55,6 +55,7 @@ def load_video_from_list(list_path):
         video_details['preset_speed'] = PRESET_SPEED
 
         train_frames, w, h = load_frames_from_video(os.path.join("test_data/", VIDEO_FILE), NUM_FRAMES * TRAIN_SAMPLES)
+        
         all_frames.extend(train_frames)
         all_details.append({
             "frames": train_frames,
@@ -69,7 +70,7 @@ def load_video_from_list(list_path):
 def generate_frame_sequences(frames):
     sequences = []
     labels = []
-    for i in range(len(frames) - NUM_FRAMES + 2):
+    for i in range(len(frames) - NUM_FRAMES + 1):
         sequence = frames[i:i+NUM_FRAMES-1]
         sequences.append(sequence)
         labels.append(sequence[-1])
@@ -87,39 +88,73 @@ def main():
 
     model = VideoCompressionModel(NUM_CHANNELS, NUM_FRAMES)
     model.compile(loss='mean_squared_error', optimizer='adam')
-
     early_stop = EarlyStopping(monitor='val_loss', patience=3, verbose=1, restore_best_weights=True)
 
+    # Load and concatenate all sequences and labels
+    all_train_sequences = []
+    all_val_sequences = []
+    all_train_labels = []
+    all_val_labels = []
+    all_crf_train = []
+    all_crf_val = []
+    all_preset_speed_train = []
+    all_preset_speed_val = []
+    
     for video_details_train, video_details_val in zip(all_video_details_train, all_video_details_val):
         train_frames = video_details_train["frames"]
         val_frames = video_details_val["frames"]
 
         train_differences = frame_difference(preprocess(train_frames))
         val_differences = frame_difference(preprocess(val_frames))
+        
+        #print(len(train_differences), train_differences[0].shape)
 
         train_sequences, train_labels = generate_frame_sequences(train_differences)
         val_sequences, val_labels = generate_frame_sequences(val_differences)
 
-        num_sequences_train = len(train_sequences)
-        num_sequences_val = len(val_sequences)
-        crf_array_train = np.full((num_sequences_train, 1), video_details_train['crf'])
-        crf_array_val = np.full((num_sequences_val, 1), video_details_val['crf'])
-        preset_speed_array_train = np.full((num_sequences_train, 1), video_details_train['preset_speed'])
-        preset_speed_array_val = np.full((num_sequences_val, 1), video_details_val['preset_speed'])
+        crf_array_train = np.full((len(train_sequences), 1), video_details_train['crf'])
+        crf_array_val = np.full((len(val_sequences), 1), video_details_val['crf'])
+        preset_speed_array_train = np.full((len(train_sequences), 1), video_details_train['preset_speed'])
+        preset_speed_array_val = np.full((len(val_sequences), 1), video_details_val['preset_speed'])
 
-        print(len(train_sequences))
-        print(len(val_sequences))
+        all_train_sequences.extend(train_sequences)
+        all_val_sequences.extend(val_sequences)
+        all_train_labels.extend(train_labels)
+        all_val_labels.extend(val_labels)
+        all_crf_train.extend(crf_array_train)
+        all_crf_val.extend(crf_array_val)
+        all_preset_speed_train.extend(preset_speed_array_train)
+        all_preset_speed_val.extend(preset_speed_array_val)
 
-        print("\nTraining the model for video:", video_details_train["video_file"])
-        model.fit(
-            {"frames": train_sequences, "crf": crf_array_train, "preset_speed": preset_speed_array_train},
-            train_labels,
-            batch_size=BATCH_SIZE,
-            epochs=EPOCHS,
-            validation_data=({"frames": val_sequences, "crf": crf_array_val, "preset_speed": preset_speed_array_val}, val_labels),
-            callbacks=[early_stop]
-        )
-        print("\nTraining completed for video:", video_details_train["video_file"])
+    # Convert lists to numpy arrays
+    all_train_sequences = np.array(all_train_sequences)
+    all_val_sequences = np.array(all_val_sequences)
+    all_train_labels = np.array(all_train_labels)
+    all_val_labels = np.array(all_val_labels)
+    all_crf_train = np.array(all_crf_train)
+    all_crf_val = np.array(all_crf_val)
+    all_preset_speed_train = np.array(all_preset_speed_train)
+    all_preset_speed_val = np.array(all_preset_speed_val)
+
+    # Shuffle the training data
+    indices_train = np.arange(all_train_sequences.shape[0])
+    np.random.shuffle(indices_train)
+
+    all_train_sequences = all_train_sequences[indices_train]
+    all_train_labels = all_train_labels[indices_train]
+    all_crf_train = all_crf_train[indices_train]
+    all_preset_speed_train = all_preset_speed_train[indices_train]
+
+    print("\nTraining the model on mixed sequences...")
+    model.fit(
+        {"frames": all_train_sequences, "crf": all_crf_train, "preset_speed": all_preset_speed_train},
+        all_train_labels,
+        batch_size=BATCH_SIZE,
+        epochs=EPOCHS,
+        validation_data=({"frames": all_val_sequences, "crf": all_crf_val, "preset_speed": all_preset_speed_val}, all_val_labels),
+        callbacks=[early_stop]
+    )
+    print("\nTraining completed!")
 
     save_model(model, 'model_differencing.keras')